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Journal articles on the topic 'Research Paper Recommendation'
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Gao, Huan, Xi Tian, and Xiang Ling Fu. "Research on Location-Based Personalized Recommendation System." Applied Mechanics and Materials 490-491 (January 2014): 1493–96. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1493.
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With the mobile Internet developing in China, the problem of information overload has been brought to us. The traditional personalized recommendation cannot meet the needs of the mobile Internet. In this paper, the recommendation algorithm is mainly based on the collaborative filtering, but the new factors are introduced into the recommendation system. The new system takes the user's location and friends recommendation into the personalized recommendation system so that the recommendation system can meet the mobile Internet requirements. Besides, this paper also puts forward the concept of moving business circle for information filtering, which realizes the precise and real-time personalized recommendations. This paper also proves the recommendation effects through collecting and analyzing the data, which comes from the website of dianping.com.
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Habib, Raja, and Muhammad Tanvir Afzal. "Sections-based bibliographic coupling for research paper recommendation." Scientometrics 119, no. 2 (2019): 643–56. http://dx.doi.org/10.1007/s11192-019-03053-8.
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Vinay Kumar Saini and Jai Raj Singh, Dr ML Sharma C. "Recommendation System." International Journal for Modern Trends in Science and Technology 6, no. 12 (2021): 484–92. http://dx.doi.org/10.46501/ijmtst061294.
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Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collab- orative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommen- dations. The novelty of our proposed approach is that it provides personalized recommen- dations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.
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Li, Zhi, and Xiaozhu Zou. "A Review on Personalized Academic Paper Recommendation." Computer and Information Science 12, no. 1 (2019): 33. http://dx.doi.org/10.5539/cis.v12n1p33.
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With the advent of the era of big data, it has become extremely easy for scientific users to have to access academic papers, which has enhanced their efficiency and capacity to search or browse papers. However, it also faces some problems such as the explosion of the literature or information overwhelming. Many researchers focus on academic paper recommendation service, hoping to help scientific users to find documents more efficiently and recommend interested or potentially interested papers which could assist academic users doing research. Through literature review, this paper make a comprehensive summary of the research on personalized academic papers recommendation, presenting the state-of-art of academic paper recommendation methodologies, pointing out its pros and cons and indicating primary evaluation metrics and popular datasets. Finnaly, we outlook the research trend of personalized academic paper recommendation as a reference for interested researchers.
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Li, Yu Long, Ying Li, Wei Jiang, and Zhi Zhou. "Research on a Theatre Recommendation System." Advanced Materials Research 989-994 (July 2014): 4775–79. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4775.
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Nowadays the recommendation system has been widely used, especially in the field of e-commerce, SNS, music, etc. On the basis of recommendation systems which are widely used, the paper puts forward a theatre recommendation algorithm which is more suitable in the field of theatre. In order to achieve the recommendation of theatre, the paper uses a series of steps, including weight, bipartite graph, data standardization, similarity calculation. After using this algorithm, some theatres will be recommended according to recommendation level. The results of recommendation are more reasonable, effective and satisfied.
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Dong, Biao. "Research on Mobile Learning Based on a Feedback Model." Applied Mechanics and Materials 678 (October 2014): 653–56. http://dx.doi.org/10.4028/www.scientific.net/amm.678.653.
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This paper presents an approach for modeling the mobile learning applications using a feedback-based recommendation model. Formal definitions are proposed for the mobile learning activity. The design unites three of the mobile learning's aspects, namely learner, service and context, and provides means for learner service evaluation within the recommendation model. The results show that the recommendation model can easily be constructed, while enabling accurate recommendations by solving the sparsity problem of service and learner′s information.
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Vellino, Andre. "Recommending research articles using citation data." Library Hi Tech 33, no. 4 (2015): 597–609. http://dx.doi.org/10.1108/lht-06-2015-0063.
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Purpose – The purpose of this paper is to present an empirical comparison between the recommendations generated by a citation-based recommender for research articles in a digital library with those produced by a user-based recommender (ExLibris “bX”). Design/methodology/approach – For these computer experiments 9,453 articles were randomly selected from among 6.6 M articles in a digital library as starting points for generating recommendations. The same seed articles were used to generate recommendations in both recommender systems and the resulting recommendations were compared according to the “semantic distance” between the seed articles and the recommended ones, the coverage of the recommendations and the spread in publication dates between the seed and the resulting recommendations. Findings – Out of the 9,453 test runs, the recommendation coverage was 30 per cent for the user-based recommender vs 24 per cent for the citation-based one. Only 12 per cent of seed articles produced recommendations with both recommenders and none of the recommended articles were the same. Both recommenders yielded recommendations with about the same semantic distance between the seed article and the recommended articles. The average differences between the publication dates of the recommended articles and the seed articles is dramatically greater for the citation-based recommender (+7.6 years) compared with the forward-looking user-based recommender. Originality/value – This paper reports on the only known empirical comparison between the Ex Librix “bX” recommendation system and a citation-based collaborative recommendation system. It extends prior preliminary findings with a larger data set and with an analysis of the publication dates of recommendations for each system.
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Li, Xing Yuan, and Qing Shui Li. "An Improved Personalized Recommendation System Research." Advanced Materials Research 756-759 (September 2013): 1398–402. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1398.
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In order to find information of interest and found valuable information resources in enrich Internet data. This paper describes a personalized recommendation system, personalized recommendation system is an intelligent recommendation system to help e-commerce site for customers to provide complete personalized shopping decision support and information services. for the User Rating data extreme sparseness, This paper presents nearest neighbor collaborative filtering algorithm based on project score predicted ,experiments show that this method can improve the quality of recommendation system.
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Lat, Ishaq, Christopher Paciullo, Mitchell J. Daley, et al. "Position Paper on Critical Care Pharmacy Services (Executive Summary): 2020 Update." American Journal of Health-System Pharmacy 77, no. 19 (2020): 1619–24. http://dx.doi.org/10.1093/ajhp/zxaa217.
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Abstract Objectives Provide a multiorganizational statement to update the statement from a paper in 2000 about critical care pharmacy practice and make recommendations for future practice. Design The Society of Critical Care Medicine, American College of Clinical Pharmacy Critical Care Practice and Research Network, and the American Society of Health-System Pharmacists convened a joint task force of 15 pharmacists representing a broad cross-section of critical care pharmacy practice and pharmacy administration, inclusive of geography, critical care practice setting, and roles. The Task Force chairs reviewed and organized primary literature, outlined topic domains, and prepared the methodology for group review and consensus. A modified Delphi method was used until consensus (>66% agreement) was reached for each practice recommendation. Previous position statement recommendations were reviewed and voted to either retain, revise, or retire. Recommendations were categorized by level of ICU service to be applicable by setting, and grouped into five domains: patient care, quality improvement, research and scholarship, training and education, and professional development. Main Results There are 82 recommendation statements: forty-four original recommendations and 38 new recommendation statements. Thirty-four recommendations were made for patient care, primarily relating to critical care pharmacist duties and pharmacy services. In the quality improvement domain, 21 recommendations address the role of the critical care pharmacist in patient and medication safety, clinical quality programs, and analytics. Nine recommendations were made in the domain of research and scholarship. Ten recommendations are in the domain of training and education and eight recommendations regarding professional development. Conclusions The statements recommended by this taskforce delineate the activities of a critical care pharmacist and the scope of pharmacy services within the ICU. Effort should be made from all stakeholders to implement the recommendations provided, with continuous effort toward improving the delivery of care for critically ill patients.
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Nishioka, Chifumi, Jörn Hauke, and Ansgar Scherp. "Influence of tweets and diversification on serendipitous research paper recommender systems." PeerJ Computer Science 6 (May 18, 2020): e273. http://dx.doi.org/10.7717/peerj-cs.273.
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In recent years, a large body of literature has accumulated around the topic of research paper recommender systems. However, since most studies have focused on the variable of accuracy, they have overlooked the serendipity of recommendations, which is an important determinant of user satisfaction. Serendipity is concerned with the relevance and unexpectedness of recommendations, and so serendipitous items are considered those which positively surprise users. The purpose of this article was to examine two key research questions: firstly, whether a user’s Tweets can assist in generating more serendipitous recommendations; and secondly, whether the diversification of a list of recommended items further improves serendipity. To investigate these issues, an online experiment was conducted in the domain of computer science with 22 subjects. As an evaluation metric, we use the serendipity score (SRDP), in which the unexpectedness of recommendations is inferred by using a primitive recommendation strategy. The results indicate that a user’s Tweets do not improve serendipity, but they can reflect recent research interests and are typically heterogeneous. Contrastingly, diversification was found to lead to a greater number of serendipitous research paper recommendations.
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Chen, Qing Zhang, Yu Jie Pei, Yan Jin, and Li Yan Zhang. "Research on Intelligent Recommendation Method and its Application on Internet Bookstore." Advanced Materials Research 121-122 (June 2010): 447–52. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.447.
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As the current personalized recommendation systems of Internet bookstore are limited too much in function, this paper build a kind of Internet bookstore recommendation system based on “Strategic Data Mining”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. Then the method clusters the customer and type of book, and gives some strategies of personalized recommendation. Internet bookstore recommendation system is implemented with ASP.NET in this article. The experimental results indicate that the Internet bookstore recommendation system is feasible.
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Gao, Jie. "Research on Goods Recommendation Strategy Based on Decision Tree." Applied Mechanics and Materials 687-691 (November 2014): 2718–21. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2718.
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Firstly, associative-sets-based collaborative filtering algorithm is proposed. During the process of personalized recommendation, some items evaluated by users are performed by accident, in other words, they have little correlation with users' real preferences. These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering. A personalized recommendation algorithm based on Associative Sets is proposed in this paper to solve this problem. It uses frequent it sets to get associative sets, and makes recommendations according to users' real preferences, so as to enhance the accuracy of recommending results. Test results show that the new algorithm is more accurate than the traditional. Secondly, a flexible E-Commerce recommendation system is built. Traditional recommendation system is a sole tool with only one recommendation model. In e-commerce environment, commodities are very rich, personal demands are diversification; E-Commerce systems in different occasions require different types of recommended strategies. For that, we analysis the recommendation system with flexible theory, and proposed a flexible e-commerce recommendation system. It maps the implementation and demand through strategy module, and the whole system would be design as standard parts to adapt to the change of the recommendation strategy.
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Jia, Hong Wei, and Fang Zhao. "Research on Customized Indoor Route Recommendation." Applied Mechanics and Materials 470 (December 2013): 753–57. http://dx.doi.org/10.4028/www.scientific.net/amm.470.753.
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Research in this paper focuses on route recommendation for customers in shopping malls for a LBS(Location-based service ) system. Besides the shopping places selected by a customer, this system can recommend shopping places according to customers history record and similar customers history record analysis. In addition, the length of all routes between two walking destinations, and the traffic density on the routes are two primary factors when route optimization is done in the system. The algorithm referred above have been implemented. A large number of testing showed that the algorithm was efficient to meet the actual need. !
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Deng, Yongjie, Yong Liu, and Dongping Tang. "Research on healthy catering recommendation space." MATEC Web of Conferences 309 (2020): 03011. http://dx.doi.org/10.1051/matecconf/202030903011.
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This paper conducts an in-depth study on the catering recommendation space based on different situations. By consulting the literature, visiting relevant experts, condensing the theoretical knowledge of TCM (Traditional Chinese Medicine) constitutional dietotherapy theory, it has extracted some key factors that can be effectively utilized in catering theory. The variable framework of the situational catering recommendation system is preliminarily constructed. Also, it has designed a mixed catering recommendation space that combines TCM Constitutional Theory and user situation, which not only meets individual preferences but also improves the users’ sub-health status. Finally, through the questionnaire survey, the importance of each attribute in the eyes of consumers has been recognized; then this essay provides a guidance for the recommendation ranking method in the design of the catering recommendation system after making the cross-analysis.
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Jiang, Ji, and Jian Gang Tang. "Research on Intelligent Knowledge Recommendation System for Police Applications." Applied Mechanics and Materials 530-531 (February 2014): 447–51. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.447.
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This paper proposed knowledge content tag recommendation algorithm in cloud computing Environment, and applied to police information knowledge. The algorithm analyzed user behavior history of operation and considered the similarity knowledge of the entries on the tag of police information, marked weight of tag in predicting when a user rating. On this basis, the police information implementations specific recommendations based on the specific user application knowledge. Meanwhile, combined the tag of system entry contents correlation with user correlation analysis, and solved the problems of system sparse matrix. Finally, the results demonstrated the effectiveness and superiority of recommendation algorithm.
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Zhang, Yan, and Tao Kuang. "The Research of E-Commerce Personalized Recommendation." Applied Mechanics and Materials 556-562 (May 2014): 6762–65. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6762.
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With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site,the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.
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Manju G., Abhinaya P., Hemalatha M.R., Manju Ganesh G., and Manju G.G. "Cold Start Problem Alleviation in a Research Paper Recommendation System Using the Random Walk Approach on a Heterogeneous User-Paper Graph." International Journal of Intelligent Information Technologies 16, no. 2 (2020): 24–48. http://dx.doi.org/10.4018/ijiit.2020040102.
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Recommendation approaches generally fail to recommend newly-published papers as relevant, owing to the lack of prior information about the said papers and, more particularly, problems associated with cold starts. It would appear, to all intents and purposes, that researchers currently interact more on social networks than they normally would in academic circles, and relationships of a purely academic nature have witnessed a paradigm shift, in keeping with this new trend. In existing paper recommendation methods, the social interaction factor has yet to play a pivotal role. The authors propose a social network-based research paper recommendation method, that alleviates cold start problems by incorporating users' social interaction, as well as topical relevancy, among assorted papers in the Mendeley academic social network using a novel approach, random walk Ergodic Markov Chain. The system yields improved results after cold start alleviation, compared with the existing system.
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Wu, Bing, and Ping Ping Chen. "Personalized Recommendation Research in E-Learning Systems." Applied Mechanics and Materials 433-435 (October 2013): 603–6. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.603.
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The purpose of this paper is to review the literatures which have made an explicit study on personalized recommendation in E-Learning systems. By identifying the important research areas, which are in different perspectives, firstly, filtering recommendation is introduced before the illustration of how it has been developed in E-Learning systems. Then personalized recommendation is proposed for E-Learning system. Although social network is the basic way to improve the communication efficiency with others in E-Learning system, previous studies pay less attention on this. Therefore social network analysis should be taken into consideration for the recommendation in E-Learning system for further research.
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Yang, Xu, Ziyi Huan, Yisong Zhai, and Ting Lin. "Research of Personalized Recommendation Technology Based on Knowledge Graphs." Applied Sciences 11, no. 15 (2021): 7104. http://dx.doi.org/10.3390/app11157104.
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Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs (KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.
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Guo, Jian Xin, and Ji Chun Zhao. "Research on Personalized Courseware Recommendation System of Rural Distance Learning Based on Combination Recommendation Technology." Applied Mechanics and Materials 373-375 (August 2013): 1652–60. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1652.
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This paper proposed a model of combination recommendation, focusing on analysis and comparison of content filtering recommendation technology and collaborative filtering recommendation technology based on the mainstream personalized recommendation technology, and the model working process is given. For how to solve the problem of sparse and cold start, the paper gave the solve methods, and discussed the process of combination recommendation algorithm, and then introduced a method of developing and designing personalized courseware recommendation system of rural modern distance learning, and introduced the optimization measures of functional modules and performance. It provides a useful reference for distance education site to carry out personalized training services for rural adult users.
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Guo, Guibing, Bowei Chen, Xiaoyan Zhang, Zhirong Liu, Zhenhua Dong, and Xiuqiang He. "Leveraging Title-Abstract Attentive Semantics for Paper Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 67–74. http://dx.doi.org/10.1609/aaai.v34i01.5335.
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Paper recommendation is a research topic to provide users with personalized papers of interest. However, most existing approaches equally treat title and abstract as the input to learn the representation of a paper, ignoring their semantic relationship. In this paper, we regard the abstract as a sequence of sentences, and propose a two-level attentive neural network to capture: (1) the ability of each word within a sentence to reflect if it is semantically close to the words within the title. (2) the extent of each sentence in the abstract relative to the title, which is often a good summarization of the abstract document. Specifically, we propose a Long-Short Term Memory (LSTM) network with attention to learn the representation of sentences, and integrate a Gated Recurrent Unit (GRU) network with a memory network to learn the long-term sequential sentence patterns of interacted papers for both user and item (paper) modeling. We conduct extensive experiments on two real datasets, and show that our approach outperforms other state-of-the-art approaches in terms of accuracy.
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Zhong, Jiemin, Haoran Xie, and Fu Lee Wang. "The research trends in recommender systems for e-learning." Asian Association of Open Universities Journal 14, no. 1 (2019): 12–27. http://dx.doi.org/10.1108/aaouj-03-2019-0015.
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Purpose A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems. Design/methodology/approach The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system. Findings The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations. Originality/value The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.
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Xiao, Liang, Hangxiao Mao, and Shu Wang. "Research on Mobile Marketing Recommendation Method Incorporating Layout Aesthetic Preference for Sustainable m-Commerce." Sustainability 12, no. 6 (2020): 2496. http://dx.doi.org/10.3390/su12062496.
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The strong interactivity and size limitation of the mobile interface calls for the utilization of users’ aesthetic preferences to provide better mobile marketing recommendations in order to promote the sustainable development of m-commerce. Existing studies mostly focus on matching user interests by analyzing marketing content properties. The studies for utilizing the layout information and user aesthetic preferences for the layout of the mobile marketing interface from an aesthetic perspective are insufficient. This paper proposes a mobile marketing recommendation method (LAPR) that incorporates layout aesthetic preferences. Based on the traditional content-based and collaborative filtering recommendation methods, this method introduces users’ aesthetic preferences for interface layout into a mobile marketing recommender. From an aesthetic perspective, a new interface layout design quantification method, a user aesthetic preference similarity measurement model, and a recommendation result ranking method are designed. Experiments show that compared to traditional methods, LAPR is significantly higher in recommendation precision in the task for recommending the same content and outperforms traditional methods in recall, precision, and F-metrics in the common recommendation task. We conclude that incorporating aesthetic preference for layout can improve mobile marketing recommendation quality and promote the sustainable development of m-commerce.
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Waheed, Waleed, Muhammad Imran, Basit Raza, Ahmad Kamran Malik, and Hasan Ali Khattak. "A Hybrid Approach Toward Research Paper Recommendation Using Centrality Measures and Author Ranking." IEEE Access 7 (2019): 33145–58. http://dx.doi.org/10.1109/access.2019.2900520.
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Wu, Ziteng, Chengyun Song, Yunqing Chen, and Lingxuan Li. "A review of recommendation system research based on bipartite graph." MATEC Web of Conferences 336 (2021): 05010. http://dx.doi.org/10.1051/matecconf/202133605010.
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The interaction history between users and items is usually stored and displayed in the form of bipartite graphs. Neural network recommendation based on the user-item bipartite graph has a significant effect on alleviating the long-standing data sparseness and cold start of the recommendation system. The whole paper is based on the bipartite graph. An review of the recommendation system of graphs summarizes the three characteristics of graph neural network processing bipartite graph data in the recommendation field: interchangeability, Multi-hop transportability, and strong interpretability. The biggest contribution of the full paper is that it summarizes the general framework of graph neural network processing bipartite graph recommendation from the models with the best recommendation effect in the past three years: embedding layer, propagation update layer, and prediction layer. Although there are subtle differences between different models, they are all this framework can be applied, and different models can be regarded as variants of this general model, that is, other models are fine-tuned on the basis of this framework. At the end of the paper, the latest research progress is introduced, and the main challenges and research priorities that will be faced in the future are pointed out.
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Alsini, Areej, Du Q. Huynh, and Amitava Datta. "Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review." Future Internet 13, no. 5 (2021): 129. http://dx.doi.org/10.3390/fi13050129.
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Hashtag recommendation suggests hashtags to users while they write microblogs in social media platforms. Although researchers have investigated various methods and factors that affect the performance of hashtag recommendations in Twitter and Sina Weibo, a systematic review of these methods is lacking. The objectives of this study are to present a comprehensive overview of research on hashtag recommendation for tweets and present insights from previous research papers. In this paper, we search for articles related to our research between 2010 and 2020 from CiteSeer, IEEE Xplore, Springer and ACM digital libraries. From the 61 articles included in this study, we notice that most of the research papers were focused on the textual content of tweets instead of other data. Furthermore, collaborative filtering methods are seldom used solely in hashtag recommendation. Taking this perspective, we present a taxonomy of hashtag recommendation based on the research methodologies that have been used. We provide a critical review of each of the classes in the taxonomy. We also discuss the challenges remaining in the field and outline future research directions in this area of study.
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Zhang, Zhijun, Gongwen Xu, and Pengfei Zhang. "Research on E-Commerce Platform-Based Personalized Recommendation Algorithm." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/5160460.
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Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms.
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Guan, Congying, Shengfeng Qin, Wessie Ling, and Guofu Ding. "Apparel recommendation system evolution: an empirical review." International Journal of Clothing Science and Technology 28, no. 6 (2016): 854–79. http://dx.doi.org/10.1108/ijcst-09-2015-0100.
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Purpose With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market. Design/methodology/approach This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords. Findings This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors’ research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system. Originality/value Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.
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Huang, Xiao, Pengjie Ren, Zhaochun Ren, et al. "Report on the international workshop on natural language processing for recommendations (NLP4REC 2020) workshop held at WSDM 2020." ACM SIGIR Forum 54, no. 1 (2020): 1–5. http://dx.doi.org/10.1145/3451964.3451970.
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This paper summarizes the outcomes of the International Workshop on Natural Language Processing for Recommendations (NLP4REC 2020), held in Houston, USA, on February 7, 2020, during WSDM 2020. The purpose of this workshop was to explore the potential research topics and industrial applications in leveraging natural language processing techniques to tackle the challenges in constructing more intelligent recommender systems. Specific topics included, but were not limited to knowledge-aware recommendation, explainable recommendation, conversational recommendation, and sequential recommendation.
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Han, Dan, Bing Liu, and Yan Sun. "The Research on Collaborative Filtering in Personalization Recommendation System." Advanced Materials Research 846-847 (November 2013): 1137–40. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1137.
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This paper does a performance comparison and evaluation to the CF algorithm based on the cosine similarity, the correlation similarity and project rating, and analyzes and researches its application, facing problems, solutions in the personalization recommendation system.
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Li, Xiaofeng, and Dong Li. "An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy." Mobile Information Systems 2019 (May 7, 2019): 1–11. http://dx.doi.org/10.1155/2019/3560968.
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The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.
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Wang, Feng, Lingling Zhang, and Xin Xu. "A LITERATURE REVIEW AND CLASSIFICATION OF BOOK RECOMMENDATION RESEARCH." Journal of Information System and Technology Management 5, no. 16 (2020): 15–34. http://dx.doi.org/10.35631/jistm.516002.
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The act of reading has benefits for individuals and societies, which can be a long-term commitment. While the overload of books information and readers’ specific needs make book recommendation (BR) in demand, BR is receiving great attention from the research community with different perspectives. The increasing amount of research conducted with BR calls for a classification methodology regarding trends and distribution in this field. This paper presents a study of recommender systems in the domain of BR. The main goal of this work is to provide authors with insights on the trends of academic literature reviews in the proposed context and to present a comparison of different research approaches. The authors searched for up-to-date research papers related to recommender systems for BR within a time period of eighteen years, from 2000 to 2018. Starting from 2000, a significant amount of research related to the subject field of recommender systems was conducted, which led to the first ACM Conference on Recommender Systems. After the filtering process, 39 papers were finally selected from journals, conferences and theses in five different academic databases (i.e. IEEE, ACM, Science Direct, Springer and ProQuest). The general classification is presented in this work, in order to describe the recommendation approaches for BR. This work can be extended in the future to include novel methodologies and trends of recommender systems for BR or other fields.
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Liu, Hanwen, Huaizhen Kou, Chao Yan, and Lianyong Qi. "Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph." Complexity 2020 (April 24, 2020): 1–15. http://dx.doi.org/10.1155/2020/2085638.
Full textAbstract:
Nowadays, scholar recommender systems often recommend academic papers based on users’ personalized retrieval demands. Typically, a recommender system analyzes the keywords typed by a user and then returns his or her preferred papers, in an efficient and economic manner. In practice, one paper often contains partial keywords that a user is interested in. Therefore, the recommender system needs to return the user a set of papers that collectively covers all the queried keywords. However, existing recommender systems only use the exact keyword matching technique for recommendation decisions, while neglecting the correlation relationships among different papers. As a consequence, it may output a set of papers from multiple disciplines that are different from the user’s real research field. In view of this shortcoming, we propose a keyword-driven and popularity-aware paper recommendation approach based on an undirected paper citation graph, named PRkeyword+pop. At last, we conduct large-scale experiments on the real-life Hep-Th dataset to further demonstrate the usefulness and feasibility of PRkeyword+pop. Experimental results prove the advantages of PRkeyword+pop in searching for a set of satisfactory papers compared with other competitive approaches.
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Wang, Qian, Jin Zhen Ping, Li Li Yu, and Zhi Juan Wang. "Research on the Model of Personalized Recommendation System Based on Multi Agent." Applied Mechanics and Materials 644-650 (September 2014): 3016–19. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3016.
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There are some lacks of intelligence, self-adaptability, initiative and processing power limitations in the traditional recommendation system. Using the multi-agent technology and the web log mining technology, this paper converts the function modules of traditional personalized recommendation system into an agent. This paper proposes an architecture model based on multi-agent e-commerce personalized recommendation system (MAPRS), and discusses the function of each component of the model and the system's running processes.
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Kaur, Lovedeep, and Naveen Kumari. "A Research on user Recommendation System Based upon Semantic Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 11 (2017): 72. http://dx.doi.org/10.23956/ijarcsse.v7i11.471.
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Recommender system applied various techniques and prediction algorithm to predict user interest on information, items and services from the tremendous amount of available data on the internet. Recommender systems are now becoming increasingly important to individual users, businesses and specially e-commerce for providing personalized recommendations. Recommender systems have been evaluated and improved in many, often incomparable, ways. In this paper, we review the evaluation and improvement techniques for improving overall performance of recommendation systems and proposing a semantic analysis based approach for clustering based collaborative filtering to improve the coverage of recommendation. The basic algorithm or predictive model we use are – simple linear regression, k-nearest neighbours(kNN), naives bayes, support vector machine. We also review the pearson correlation coefficient algorithm and an associative analysis-based heuristic. The algorithms themselves were implemented from abstract class recommender, which was extended from weka distribution classifier class. The abstract class adds prediction method to the classifier.
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Tao, Wanqiong, Chunhua Ju, and Chonghuan Xu. "Research on Relationship Strength under Personalized Recommendation Service." Sustainability 12, no. 4 (2020): 1459. http://dx.doi.org/10.3390/su12041459.
Full textAbstract:
Relationship of users in an online social network can be applied to promote personalized recommendation services. The measurement of relationship strength between user pairs is crucial to analyze the user relationship, which has been developed by many methods. An issue that has not been fully addressed is that the interaction behavior of individuals subjected to the activity field preference and interactive habits will affect interactive behavior. In this paper, the three-way representation of the activity field is given firstly, the contribution weight of the activity filed preferences is measured based on the interactions in the positive and boundary regions. Then, the interaction strength is calculated, integrating the contribution weight of the activity field preference and interactive habit. Finally, user relationship strength is calculated by fusing the interaction strength, common friend rate and similarity of feature attribute. The experimental results show that the proposed method can effectively improve the accuracy of relationship strength calculation.
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Yang, Qing, Peiling Yuan, and Xi Zhu. "Research of Personalized Course Recommended Algorithm based on the Hybrid Recommendation." MATEC Web of Conferences 173 (2018): 03067. http://dx.doi.org/10.1051/matecconf/201817303067.
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This paper presents a personalized course recommended algorithm based on the hybrid recommendation. The recommendation algorithm uses the improved NewApriori algorithm to implements the association rule recommendation, and the user-based collaborative filtering algorithm is the main part of the algorithm. The hybrid algorithm adds the weight to the recommendation result of the user-based collaborative filtering and association rule recommendation, implementing a hybrid recommendation algorithm based on both of them. It has solved the problem of data sparsity and cold-start partially and provides a academic reference for the design of high performance elective system. The experiment uses the student scores data of a college as the test set and analyzes results and recommended quality of personalized elective course. According to the results of the experimental results, the quality of the improved hybrid recommendation algorithm is better.
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Song, Jie, and Qing Song. "Research and Application on Recommendation Trust Model in Distributed Network System." Applied Mechanics and Materials 687-691 (November 2014): 2063–66. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2063.
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In distributed network, a node requesting recommendation needs to select right recommendation node. This paper puts forward a recommendation trust model. Compared with recommended capacity trust, this model distinguishes service quality trust from it , including nodes’ correlations, frequencies and risk. Among them, correlations are measured by service care and assessment capacity similarity, frequencies and risk are calculated due to various nodes.At last, an example verified the usefulness of this model, and results show that the model not only has high accuracy when select recommendation nodes, but also can improve the nodes interactions’ satisfaction rate.
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Li, Jinhai, Yunlei Ma, Xiang Zhan, and Jiaming Pei. "Research of Contextual Semantic Reasoning Model Based on Domain Ontology." Scientific Programming 2021 (September 13, 2021): 1–9. http://dx.doi.org/10.1155/2021/4011190.
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With the development of mobile network technology and the popularization of mobile terminals, traditional information recommendation systems are gradually changing in the direction of real-time and mobile information recommendation. Information recommendation brings the problem of user contextual sensitivity within the mobile environment. For this problem, first, this paper constructs a domain ontology, which is applicable to the contextual semantic reasoning model. Second, based on the “5W + 1H” method, this paper constructs a context pedigree of the mobile environment using a model framework of a domain ontology. The contextual factors of the mobile environment are divided into six categories: the What-object context, the Where-place context, the When-time context, the Who-subject context, the Why-reason context, and the How-effect context. Then, considering the degree of influence of each contextual factor from the mobile context pedigree to the user is different, this paper uses contextual conditional entropy to calculate the contextual weight of each contextual attribute in the recommendation process. Based on this, a contextual semantic reasoning model based on a domain ontology is constructed. Finally, based on the open dataset provided by GroupLens, this paper verifies the validity and efficiency of the model through a simulation experiment.
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Zhang, Yan. "E-Commerce Personalized Recommendation." Advanced Materials Research 989-994 (July 2014): 4996–99. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4996.
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With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site, the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.
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Dai, Hong Qin. "The Research on Intelligent Clothing Recommendation System Based on Ontology." Advanced Materials Research 175-176 (January 2011): 827–31. http://dx.doi.org/10.4028/www.scientific.net/amr.175-176.827.
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Ontology which is a description of knowledge has been applied to many fields. Some intelligent systems based on ontology have been developed. In the paper, a clothing recommendation system based on ontology is developed. The recommendation system mainly includes two parts: knowledge base and inference engine. The structural knowledge of clothing is represented by using ontology and some constraint knowledge is described by SWRL. The clothing recommendation process are carried out using JESS, a rule engine for the Java platform, by mapping OWL-based clothing knowledge and SWRL-based design rules into JESS facts and JESS rules, respectively.
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Liu, Li Min, Chen Yang Zhang, Zhi Qiang Ma, and Yu Hong Xiao. "Research for Cold-Start Problem in Network-Based Recommendation Algorithm." Applied Mechanics and Materials 462-463 (November 2013): 861–67. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.861.
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Network-based recommendation algorithm presents a good recommended result in many aspects. The algorithm is also facing the problem of cold-start. This paper proposes a solution for cold-start problem which makes use of an algorithm based on items similarity to calculate the similarity between the new item and other items in the system, and then link the new item to the user-item matrix. Finally the new items can be recommended to users by the network-based recommendation algorithm what the traditional network-based recommendation algorithm can't do. Therefore, the problem is solved on certain degree.
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Liu, Li Min, Peng Xiang Zhang, Le Lin, and Zhi Wei Xu. "Research of Data Sparsity Based on Collaborative Filtering Algorithm." Applied Mechanics and Materials 462-463 (November 2013): 856–60. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.856.
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During the traditional collaborative filtering recommendation algorithm be impacted by itself data sparseness problem. It can not provide accurate recommendation result. In this paper, Using traditional collaborative filtering algorithm and the concept of similar level, take advantage of the idea of data populating to solve sparsity problem, then using the Weighted Slope One algorithm to recommend calculating. Experimental results show that the improved algorithm solved the problem of the recommendation results of low accuracy because of the sparse scoring matrix, and it improved the algorithm recommended results to a certain extent.
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Deng, Guang Biao. "Research on E-Commerce Promotion System Based on Web Data Mining Technology." Applied Mechanics and Materials 686 (October 2014): 311–15. http://dx.doi.org/10.4028/www.scientific.net/amm.686.311.
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This paper describes the use of Web data mining, and analyze the data on the web site (including the server log, commercial database, user database, the shopping cart, user mode) that access to relevant knowledge for goods, commodities such as preference relations. Secondly, the static model of the data mining methods, it is a manifestation of the site management personnel marketing thought. Based on these models, the paper proposed strategy for the site registered users, and produces the corresponding calculating formulas of a good recommendation and the corresponding recommendation algorithm for the current user, thus to get a user recommendation.
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Chen, Yen-Liang, Cheng-Hsiung Weng, Cheng-Kui Huang, and Duo-Jia Shih. "An innovative citation recommendation model for draft papers with varying degrees of information completeness." Data Technologies and Applications 53, no. 4 (2019): 562–76. http://dx.doi.org/10.1108/dta-12-2018-0105.
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Purpose As researchers are writing a draft paper with incomplete structure or text, one of burdensome tasks is to deliberate about which references should be cited for one sentence or paragraph of this draft. In view of the rapid increase in the number of research papers, researchers desire to figure out a better way to do citation recommendations in developing their draft papers. The purpose of this paper is to propose citation recommendation algorithms that enable the acquisition of relevant citations for research papers that are still at the drafting stage. This study attempts to help researchers to select appropriate references among the vast amount of available papers and make draft papers complete in reference citation. Design/methodology/approach This study adopts a model for recommending citations for incomplete drafts. Four algorithms are proposed in this study. The first and second algorithms are unsupervised models, applying term frequency-inverse document frequency and WordNet technologies, respectively. The third and fourth algorithms are based on the second algorithm to integrate different weight adjustment strategies to improve performance. Findings The proposed recommendation method adopts three techniques, including using WordNet to transform vector and setting adjustment weights according to structural factors and the information completeness degree, to generate citation recommendation for incomplete drafts. The experiments show that all these three techniques can significantly improve the recommendation accuracy. Originality/value None of the methods employed in previous studies can recommend articles as references for incomplete drafts. This paper addresses the situation that a draft paper can be incomplete either in structure or text or both. Recommended references, however, can be still generated and inserted into any desired sentence of the draft paper.
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He, Yuan Jing, Wan Lin Gao, Kun Wang, and Guan Wang. "Research of Active Recommendation Method Based on Users’ Static and Dynamic Characteristics." Applied Mechanics and Materials 263-266 (December 2012): 2687–91. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2687.
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The means of active recommendation is now increasingly applied to many different domains as it might meet users’ growing demands of personalized information service in process of selective purchasing for products, and research of recommendation technology based on user’s characteristics has been a key role in study area of active recommendation. However, investigations were performed to view there are still some deficiencies on existing methods. Therefore, an active recommendation method based on users’ static and dynamic characteristics is present and this paper proceeds as following. Firstly, feature model of user interests is constructed by analyzing static and dynamic data of target users for specific recommend. After that, neighbor users who have similar attributes with object user are found by reference to user model. Last, preference of intended user for other resources is reasonably predetermined by calculating interestingness with combination of neighbors, finally in this way can the main theory basis of recommendation comes into being. So in this paper a new approach based on users’ characteristics is provided, and it is shown with this method can effectively solve problem of shortage of satisfaction of traditional recommendation.
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Xu, Min, Mei Qi Fang, Pan Pan Yang, and Yu Chen. "Research on Personalized Learning Service Based on Collaborative Filtering Method." Advanced Materials Research 159 (December 2010): 252–57. http://dx.doi.org/10.4028/www.scientific.net/amr.159.252.
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In this paper, we discussed various personalized learning recommendation service and their advantages and disadvantages. On the basis of these methods, we proposed the similarity degree computing algorithm and user community discover algorithm. After verifying, analyzing and evaluating these algorithms and the recommendation model, we applied it as a recommendation service in SGCL (Social Group Collaborative Learning) System. Using the model in SGCL system, the system can recommend user personalized information and practical data proves that it can improve the learning quality effectively.
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Sun, Jie Li, Yun Lu, and Fu Liang Li. "Research on Multiple Cases Database Construction of Case-Based Reasoning Personalized Recommendation System." Applied Mechanics and Materials 303-306 (February 2013): 1448–51. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1448.
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The multiple cases database construction is one of the important links to design the personalized recommendation system. Personalized recommendation system case can be organized with multiple cases database based on expert experience and thinking patterns, combined with the traditional case method of organization. This paper studies the multiple cases database construction method of the personalized recommendation system based-CBR.
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Fayyaz, Zeshan, Mahsa Ebrahimian, Dina Nawara, Ahmed Ibrahim, and Rasha Kashef. "Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities." Applied Sciences 10, no. 21 (2020): 7748. http://dx.doi.org/10.3390/app10217748.
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Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.
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Zia, Kashif, Muhammad Shafi, and Umar Farooq. "Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles." Future Internet 12, no. 4 (2020): 69. http://dx.doi.org/10.3390/fi12040069.
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The latest manifestation of “all connected world" is the Internet of Things (IoT), and Internet of Vehicles (IoV) is one of the key examples of IoT these days. In Social IoV (SIoV), each vehicle is treated as a social object where it establishes and manages its own Social Network (SN). Incidentally, most of the SIoV research in the literature is related to proximity-based connectivity and interactions. In this paper, we bring people in the loop by incorporating their SNs. While emphasizing a recommendation scenario, in which vehicles may require recommendations from SNs of their owners (in addition to their own SIoV), we proposed an agent-based model of information sharing (for context-based recommendations) on a hypothetical population of smart vehicles. Some important hypotheses were tested using a realistic simulation setting. The simulation results reveal that a recommendation using weak ties is more valuable than a recommendation using strong ties in pure SIoV. The simulation results also demonstrate that recommendations using the most-connected person in the social network are not more valuable than recommendation using a random person in the social network. The model presented in this paper can be used to design a multi-scale recommendation system, which uses SIoV and a typical SN in combination.